ShipNet Voyage Estimator provides the right tools for evaluating a voyage from different perspective and making the suitable chartering decisions. It also guarantees maximum profit for charterers through the right decisions taken. It is highly crucial for a shipping company to match the appropriate cargo with the suitable vessel so that there is no any complexities and wastage of space. ShipNet Voyage Estimator provides this function to its users. As there is increasing competitiveness and enhanced
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finding a good estimator of the point ✓‚ that is‚ a good point estimator. It may also be the case that some function of ✓‚ say ⌧ (✓)‚ is of interest. The methods described below can also be used to obtain estimators of ⌧ (✓). The following definition of a point estimator may seem vague. However‚ we do not want to eliminate any candidates from consideration. Definition 1. A point estimator is any function W (X1 ‚ . . . ‚ Xn ) of a sample; that is‚ any statistic is a point estimator. 1 2 In
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ECON2206 Revision Notes W2 – SIMPLE REGRESSION MODEL MOTIVATION Much of applied econometric analysis are interested in “explaining y in terms of x” and confront three issues: 1) Since there is never an exact relationship between y and x‚ how do we account for the “other unobserved” variables? 2) What
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estimate of national income was prepared by Dadabhai Nauroji for the year 1867 - 68. Accordingly the N.I and Per Capita Income of the country were Rs. 340 crores & Rs. 20 respectively. Notable among the estimators before independence were William Digby (1899)‚ Findlay Shirras (1911‚ 1922 & 1931)‚ Shah and Khambatta (1921)‚ Dr. V.K.R.V. Rao (1925-29 & 1931-32) & R.C Desai (1931-40). To estimate National Income they have estimated the value of the
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is measured with error: respond = respond* + e (i.e.‚ observed = truth + error). Would the OLS estimators of ’s still be unbiased and consistent and why? c) Suppose that MLR.1-4 hold for the model when all variables are correctly measured. Further suppose that one regressor‚ mailsyear‚ is measured with an additive error and the error is uncorrelated with the truth mailsyear*. How would the OLS estimator of‚ say‚ β1 be affected by the measurement error and why? d) Estimate the model‚ using OLS‚
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Ballast Nedam Engineering Ing. C. (Kees) Vermeij Head of estimation Ballast Nedam Infra Projecten Abstract Companies are starting to use Building Information Models (BIMs) for cost estimation purposes. This BIM-based estimating enables estimators to quickly and accurately extract quantities and estimate construction costs‚ potentially leading to a better estimate. In projects that are more complex however‚ acquiring an accurate estimate requires more effort‚ because complexity features
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lab you have learned how to operate Stata and calculate descriptive statistics. You also read a paper with an interesting research question. Self-Lab 2 covers some topics of Lecture 2 and 3. In this lab you are going to learn how to calculate OLS estimator with your own hand. Later‚ you are going to answer some conceptual questions. These concepts are used in the practice very often and you should not make mistake while using them. Understand clearly what they are by reading from internet or any book
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at an estimator of a parameter that can be implemented into one’s research. In order to achieve this estimator‚ statisticians must first determine a model that incorporates the process being studied. Once the model is determined‚ statisticians must find any limitations placed upon an estimator. These limitations can be found through the Cramer-Rao lower bound. Under smoothness conditions‚ the Cramer-Rao lower bound gives a formula for the lower bound on the variance of an unbiased estimator. Once
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distribution. 4. Assuming no measurement error‚ the reliability of an estimate of a population parameter can be assessed in terms of its standard error. 5. The standard error of the mean can be estimated by using the sample standard deviation‚ s‚ as an estimator of . 6. z values calculate the area under the sampling distribution. 7. When the sample size is over 10% of the population size‚ the standard error formulas overestimate the standard deviation of the population parameter. A finite population
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Properties of OLS estimators Population regression line: E(y|x)=1+2x‚ Observation = systematic component + random error: yi = 1 +2 x + ui Sample regression line estimated using OLS estimators: = b1 + b2 x Observation = estimated relationship + residual: yi =+ ei => yi = b1 + b2 x + ei Assumptions underlying model: 1. Linear Model ui = yi - 1- 2xi 2. Error terms have mean = 0 E(ui|x)=0 => E(y|x) = 1 + 2xi 3. Error terms have constant variance (independent of x) Var(ui|x)
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